In our previous blog, we looked at the methods and appropriate uses for qualitative data collection. Now, we continue with a look at quantitative research design, which, like its qualitative cousin, is often quite misunderstood in terms of what it is, what it does, and why it should (or should not) be part of the overall data gathering plan.
What is Quantitative Research Design?
Because of the word “quantitative,” some people believe that quantitative research is “all about numbers.” This perception is understandable, yet fundamentally wrong –– in the same (incorrect) way that saying surgery is “all about incisions” and conducting a symphony is “all about waving a wand back and forth.” Surgery is about helping people heal, and conducting is about facilitating beautiful music. Incisions and waving (if done with expertise of course!) are essential, yet they are means to an end.
In the same way, the numbers that quantitative research design ultimately capture are not what the effort and investment is about. Rather, it is about the ACTIONABLE INSIGHTS. In other words: the analysis of those numbers, and their practical use toward smarter business decisions matters more.
Otherwise, businesses will end up with plenty of numbers –– we are talking spreadsheets, databases, lists, and so on –– but, no pun intended, they won't add up to much. That is, they do not address and resolve the research question that inspired the effort in the first place. The link between cause and effect will remain elusive, regardless of how many spreadsheets and colorful tables and charts may be on the table.
This is precisely why the “design” part of “quantitative research design” is so so important. Businesses (or professional market research firms on their behalf) must objectively and intelligently determine what they want to know, who they want to know it from, and whether/to what extent quantitative research design can meet these requirements. As data scientists and analysts frequently point out “garbage in equals garbage out.” The same basic axiom holds true with quantitative research design: “bad design in equals bad research out.”
Quantitative Research Design Methods
Good quantitative research design usually involves a customized mix of data gathering methods, such as online surveys (web, mobile and email), direct (postal) mail surveys, point-of-purchase surveys, and in some cases telephone surveys as well.
As we have discussed in various blogs, there are advantages and disadvantages to each of these methods, including those related to cost, sample size, time, data quality, analysis, automation, standardization, and so on.
Since there is no perfect method (and any consultant or so-called expert who claims otherwise should be avoided at all costs!), businesses need to find the right mix to minimize bias and data loss.
Quantitative Research Design Applications
Some common applications of quantitative research design include, but are not limited to identifying:
- What proportion of customers find a product useful, attractive, a good investment, and so on.
- What proportion of customers use the internet to conduct research or make a purchase.
- What proportion of customers would respond to a promotion or incentive (e.g. reduced price, enhanced product, longer warranty period, etc.).
- What proportion of customers are aware of a business’s full roster of products and services.
When combined with qualitative research (which explores the underlying “why” of aspects like those noted above), businesses obtain a reliable picture of what is really happening in their marketplace, and can use this insight to achieve significant, if not in many cases unprecedented gains to everything from market penetration, to customer satisfaction, to revenues and profits.
To learn more about quantitative research design, and how your business can use this approach to reap a range of rewards and measurable ROI, contact the Communications For Research team today. If you currently have a research plan, we would be pleased to review it and share ideas and insights to put you on track for long-term success!
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